Classification and recognition of dynamical models: the role of phase, independent components, kernels and optimal transport

We address the problem of performing decision tasks, and in particular classification and recognition, in the space of dynamical models in order to compare time series of data. Motivated by the application of recognition of human motion in image sequences, we consider a class of models that include linear dynamics, both stable and marginally stable (periodic), both minimum and non-minimum phase, driven by non-Gaussian processes. This requires extending existing learning and system identification algorithms to handle periodic modes and nonminimum phase behavior, while taking into account higher-order statistics of the data. Once a model is identified, we define a kernel-based cord distance between models that includes their dynamics, their initial conditions as well as input distribution. This is made possible by a novel kernel defined between two arbitrary (non-Gaussian) distributions, which is computed by efficiently solving an optimal transport problem. We validate our choice of models, inference algorithm, and distance on the tasks of human motion synthesis (sample paths of the learned models), and recognition (nearest-neighbor classification in the computed distance). However, our work can be applied more broadly where one needs to compare historical data while taking into account periodic trends, non-minimum phase behavior, and non-Gaussian input distributions.     

Polyamides incorporating furan moieties. 5. Synthesis and characterisation of furan-aromatic homologues

2,2′-Bis (5-chloroformyl 2-furyl) propane and various aromatic diamines were used as monomers in the study of their interfacial polycondensation and the properties of the ensuing furanic-aromatic polyamides. The effects of such variables as the nature of the organic phase, the temperature, the reaction time, and the type and concentration of the catalyst were investigated as well as the properties of the polyamides in terms of structure, average chain length, T_g, T_m and thermal stability.     

Capacitive micromachined ultrasonic transducer (CMUT) arrays for medical imaging

Capacitive micromachined ultrasonic transducers (CMUTs) bring the fabrication technology of standard integrated circuits into the field of ultrasound medical imaging. This unique property, combined with the inherent advantages of CMUTs in terms of increased bandwidth and suitability for new imaging modalities and high frequency applications, have indicated these devices as new generation arrays for acoustic imaging. The advances in microfabrication have made possible to fabricate, in few years, silicon-based electrostatic transducers competing in performance with the piezoelectric transducers. This paper summarizes the fabrication, design, modeling, and characterization of 1D CMUT linear arrays for medical imaging, established in our laboratories during the past 3 years. Although the viability of our CMUT technology for applications in diagnostic echographic imaging is demonstrated, the whole process from silicon die to final probe is not fully mature yet for successful practical applications.     

Modelling and Validating a multiple-configuration railway signalling system using SDL

This paper discusses some issues about the usage of SDL and related commercial SDL support tools for the validation of a railway signalling system: in particular, the issue of the multiple configurations presented by this system is addressed, discussing the possible strategies to validate the system regardless to the actual configuration.     

P-Bandwidth Priority Queues on Reconfigurable Tree of Meshes

This paper shows a parallel implementation of a priority queue with bandwidthPand maximum sizenPby means of a network with reconfigurable buses. The proposed solution is based on a tree of meshes architecture ofO(nP²) processors andO(Plogn) maximum subbus length. The computational times required by the operations of a priority queue with bandwidthPareO(1) for all the operations, using the unit-time delay model for broadcasting, while they areO(1) for MIN andO(logP+ log logn) for both DELETEMIN and INSERT, using the log-time delay model. The proposed network can be laid out in a classical H-shaped manner to occupyO(nP²) area in the VLSI model. WhenP=O(1), the required area is optimal and, using the unit-time delay model, the resultingAT²is also optimal. The paper presents also a very simple and efficient way of merging two sorted sequences on a reconfigurable mesh, which is used in the implementation of the priority queue operations.     

两种补偿动态摩擦力的先进控制策略

在小型DC伺服电机中,静摩擦力和库仑摩擦力的影响是相当明显的.本文提出 了改善系统响应特性的两种控制策略.第一种基于摩擦力模型的在线补偿.适用于变化信 号的跟踪.第二种基于自学习原理的重复控制,适用于周期信号的跟踪.两种控制策略均已 实施在计算机控制的伺服系统中.给出了与常规控制结果的对比.     

Agents in object‐oriented software engineering

Software engineers of multi‐agent systems (MASs) are faced with different concerns such as autonomy, adaptation, interaction, collaboration, learning, and mobility, which are essentially different from classical concerns addressed in object‐oriented software engineering. MAS developers, however, have relied mostly on object‐oriented design techniques and programming languages, such as Java. This often leads to a poor separation of MAS concerns and in turn to the production of MASs that are difficult to maintain and reuse. This paper discusses software engineering approaches for MASs, and presents a new method for integrating agents into object‐oriented software engineering from an early stage of design. The proposed approach encourages the separate handling of MAS concerns, and provides a disciplined scheme for their composition. Our proposal explores the benefits of aspect‐oriented software development for the incorporation of agents into object‐oriented systems. We also illustrate our aspect‐oriented approach through the Portalware multi‐agent system, a Web‐based environment for the development of e‐commerce portals. Copyright © 2004 John Wiley & Sons, Ltd.     

On the ill-conditioning of subspace identification with inputs

There is experimental evidence that the performance of standard subspace algorithms from the literature (e.g. the N4SID method) may be surprisingly poor in certain experimental conditions. This happens typically when the past signals (past inputs and outputs) and future input spaces are nearly parallel. In this paper we argue that the poor behavior may be attributed to a form of ill-conditioning of the underlying multiple regression problem, which may occur for nearly parallel regressors. An elementary error analysis of the subspace identification problem, shows that there are two main possible causes of ill-conditioning. The first has to do with near collinearity of the state and future input subspaces. The second has to do with the dynamical structure of the input signal and may roughly be attributed to “lack of excitation”. Stochastic realization theory constitutes a natural setting for analyzing subspace identification methods. In this setting, we undertake a comparative study of three widely used subspace methods (N4SID, Robust N4SID and PO-MOESP). The last two methods are proven to be essentially equivalent and the relative accuracy, regarding the estimation of the (A,C) parameters, is shown to be the same.